A Review on Topological Data Analysis in Human Activity Recognition

Abstract

Topological Data Analysis is a set of mathematical methods for capturing the structure of data forms. TDA has been used to cope with noisy signals and time series. TDA is used to discover the key characteristics of data, which are shapes. One of the basic techniques for TDA is persistent homology which is used to do a study of time series and signal processing. Human activity analysis is a well-researched issue in the field of vision, with large research on the topic. The produced time- series data is typically high-dimensional, intrinsic, non-linear, and non-stationary, leading to pattern changes. The data acquired by a person’s numerous sensors, as well as their placement and orientation, often produces noisy and complicated data, resulting in poor model performance. The implementation method must be data-driven, independent of sensor location and orientation, and employ a coordinate and parameter-free strategy to identify the variable time series data. We need technology that can represent a low-dimensional manifold compactly, identify emergent behaviors, and globalize the system. The persistent homology having property being co- ordinate free. Sensor placement has little effect on time series factorization using persistent homology. This paper gives an overview of topological approaches as well as the problems of human activity analysis, as well as recommendations for the future.